Michigan
Retirement
Research
University of
Working Paper
WP 2005-109
Center
Gender, Marriage, and Asset Accumulation
in the United States
Lucie Schmidt and Purvi Sevak
MR
RC
Project #: UM05-12
“Gender, Marriage, and Asset Accumulation in the United
States”
Lucie Schmidt
Williams College
Purvi Sevak
Hunter College
December 2005
Michigan Retirement Research Center
University of Michigan
P.O. Box 1248
Ann Arbor, MI 48104
http://www.mrrc.isr.umich.edu/
(734) 615-0422
Acknowledgements
This work was supported by a grant from the Social Security Administration through the
Michigan Retirement Research Center (Grant # 10-P-98358-5). The findings and
conclusions expressed are solely those of the author and do not represent the views of the
Social Security Administration, any agency of the Federal government, or the Michigan
Retirement Research Center.
Regents of the University of Michigan
David A. Brandon, Ann Arbor; Laurence B. Deitch, Bingham Farms; Olivia P. Maynard, Goodrich;
Rebecca McGowan, Ann Arbor; Andrea Fischer Newman, Ann Arbor; Andrew C. Richner, Grosse Pointe
Park; S. Martin Taylor, Gross Pointe Farms; Katherine E. White, Ann Arbor; Mary Sue Coleman, ex
officio
Gender, Marriage, and Asset Accumulation in the United States
Lucie Schmidt
Purvi Sevak
Abstract
Wealth accumulation has important implications for the relative well-being of
households. In this paper, we describe how household wealth in the United States varies
by gender and family type. We find evidence of large differences in observed wealth
between single-female-headed households and married couples. Although some of this
gap reflects differences in observable characteristics correlated with gender and wealth –
such as position in the life cycle, education, and family earnings – controlling for these
characteristics reduces but does not eliminate the estimated wealth gap. The wealth
holdings of single females in the U.S., controlling for these same characteristics, are also
significantly lower than the wealth holdings of single males in the U.S. In contrast,
observed wealth gaps between genders in a sub-sample of young households disappear
when controlling for observable characteristics, suggesting either that these gaps are
disappearing for younger households or that these gaps do not emerge until later in life.
Authors’ Acknowledgements
We would like to thank Carmen Diana Deere, Cheryl Doss, Emma Hutchinson, Sherrie
Kossoudji, Shelly Lundberg, Quinn Moore, Frances Rosenbluth, and four anonymous
reviewers for comments and suggestions. All errors or omissions are our own.
I. INTRODUCTION
Across many different countries, marriage has historically been viewed as a source of
financial security, particularly for women (see Linda Waite and Maggie Gallagher 2000). For
example, in the United States in 2003, 28 percent of single-female-headed households had a
family income below the poverty line, compared with 13.5 percent of single-male-headed
households and only 5.8 percent of married couple households. Wu (2005) documents that
poverty rates are significantly higher among women in all but two of the developed countries
included in the Luxembourg Income Study. A great deal of attention has been paid in economics
literature to differences in income and poverty by gender, in many developed countries but
particularly in the United States (e.g. Francine D. Blau and Lawrence Kahn 1997; June O’Neill
2003; Quinn Moore and Heidi Shierholz 2004, Kebin Wu, 2005). Much less attention has been
paid to differences in wealth accumulation along gender and family structure lines – a notable
omission because, as discussed in a recent survey paper by Edward N. Wolff in the Journal of
Economic Perspectives (1998), wealth is an important indicator of well-being for several
reasons. Wealth provides a source of direct financial income. It can provide a means for
consumption if assets are converted to cash or liquidity in times of economic stress. Wealth in
the form of owner-occupied housing provides services directly to its owners. For these reasons, it
is critical to understand how wealth holdings differ across households.
Despite the importance of wealth accumulation to the financial security of families, to
date, there has been no analysis that analyzes wealth differences in any country, by marital status
and gender. It is not clear to what extent observed differences in wealth holdings by family type
reflect underlying differences in other factors correlated with both wealth and family type – such
as education, race, or the presence of children – yet it is important to examine the source and
impact of these discrepancies, which are, in fact, far-reaching. For example, as one important
motive for wealth accumulation is to finance retirement, factors influencing wealth accumulation
1
differentials by gender and marital status may have important implications for differences in
well-being among the elderly. In this paper, we examine wealth holdings of U.S. households by
gender and family type.
Table 1 begins to shed light on this topic by providing a snapshot of wealth differences
by marital status and gender in the Panel Study of Income Dynamics (PSID), a national survey
that collects detailed household-level data on wealth. In 2001, the average total net worth of
married households ($262,929) was more than twice that of households headed by single
individuals ($112,547 for female-headed and $119,861 for male-headed). However, the
distribution of wealth is highly skewed in the US, with a large percentage of total wealth held by
a small percentage of the population, and therefore it is important to look at the relationship
between marital status, gender, and wealth throughout the wealth distribution. Table 1 shows that
the median wealth of married couple households, at $136,101, is more than four times that of
households headed by single individuals, at roughly $29,000. At the 25th percentile, the wealth of
married couples is $39,500, which is more than 20 times as large as that of the single male and
female-headed households. Figure 1 plots the distribution of wealth by family type and similarly
shows that generally, the wealth distributions of single-headed households are substantially
lower than the wealth distribution of married couples. In addition, the wealth of single
households headed by males is quite similar to single households headed by females, despite the
fact that single female households are much more likely to contain children (23 percent) than
male-headed households (13 percent).
In this paper, we use data from the PSID to provide descriptive information on how the
accumulated wealth of households varies by gender and family type in the United States. The
United States provides an interesting case study, since large increases in female education and
labor force participation, as well as a decreasing gender wage gap, might be expected to reduce
inequality in wealth. Changes in wealth inequality observed in the U.S. may be predictive of
2
changes in other developed countries. Though not presently possible, in the future, we will be
able to make comparisons across countries using the Luxembourg Wealth Study, a project which
will construct of database of comparable wealth measures across countries.
We find evidence throughout the wealth distribution of large differences in observed
wealth between households headed by single females and households headed by married
couples. Although some of this gap reflects differences in observable characteristics that are
correlated with both gender and wealth – such as position in the life cycle, education,
inheritances, and family earnings – controlling for these characteristics reduces but does not
eliminate the wealth gap. In addition, by controlling for these same characteristics, we find that
the wealth holdings of single females are also significantly lower than the wealth holdings of
single males. Results from a sub-sample of young households (with heads between 25 and 39
years of age) provide no evidence of wealth gaps by gender or family type. This finding could
reflect one of two possibilities. First, perhaps these gaps in wealth do not emerge until later in
life. Second, it is possible that the relationship between marital status, gender, and savings has
changed and that wealth gaps do not exist in the younger cohorts. Our data do not allow us to
distinguish adequately between these two possibilities.
II. BACKGROUND
Wealth, or assets A, in period t+1 can be expressed in the following manner:
At+1 = (1 + r) (At + Yt - Ct)
(1)
where r is the rate of return on investments, Yt denotes income in period t, and Ct denotes
consumption in period t. This expression illustrates that differences in wealth across households
can occur for three reasons. First, some households may enter the period with a greater stock of
assets, perhaps as a result of inherited wealth. Second, households may receive different rates of
3
return on their assets due to differences in portfolio allocation. Third, households may differ in
the amount they save.
The amount households save will vary for a variety of reasons. Standard models of the
allocation of consumption over time suggest that savings will depend on a family’s stage in the
life cycle (Franco Modigliani and Richard Brumberg 1954; Milton Friedman 1957). Households
that wish to smooth their consumption over time will save during working years in order to
finance consumption after retirement. In addition, family saving for expenditures such as
children’s college education is life cycle-related. This suggests that a family’s position in the life
cycle will strongly affect their savings behavior and wealth accumulation. Empirical evidence
suggests that the life cycle savings model applies well to white, urban, educated, middle-class
wealth accumulation but does not apply to the poorest households, many of which have no
assets, or to the wealthiest households, which often inherit wealth (Edward N. Wolff 1981).
Income also affects wealth accumulation through savings. In a given period, two
households with the same savings rate but different levels of income will accumulate different
levels of wealth. Additionally, precautionary savings in the presence of risk aversion may
increase wealth accumulation in certain households (Stephen Zeldes 1989; Miles Kimball 1990).
If there is some uncertainty regarding future income, and if liquidity constraints exist that may
prevent a household from borrowing, then risk-averse households may accumulate additional
wealth now in order to prevent a future drop in consumption caused by an income shock.
Households are therefore likely to differ in saving rates depending on these preferences and their
current consumption needs in the presence of liquidity constraints.
Gender differences in wealth could result from any of the above factors. Nevertheless, a
persistent gender gap exists in earnings, so women would be expected to accumulate lower levels
4
of wealth, even holding savings rates constant.1 There is also evidence that returns to savings
might vary by gender; research suggests that women invest their portfolios more conservatively,
which may result in lower returns to wealth.2 In addition, recent work by Candida G. Brush et al.
(2002) suggests that, in the United States, a relative lack of social networks impedes women’s
access to the venture capital industry, causing women who own businesses to be left out of this
particular avenue of wealth creation.
In addition, since total net worth includes equity in a household’s main residence, any
discrimination in mortgage lending markets could lead to gender differences in wealth. Helen
Ladd (1998), in a survey of the evidence of discrimination on mortgage lending in the U.S.,
discusses explicit bank policies prior to the 1974 Equal Credit Opportunity Act (ECOA) that
treated women less favorably than men. She reports that evidence of differential treatment of
women in mortgage lending was visible in data from the early 1970s but seems to have
disappeared after passage of the ECOA prohibited sex-based classifications in lending. However,
recent work by Judith Robinson (2002) finds evidence in mortgage lending of gender and family
structure discrimination that depends on race. Her results imply that white couples face
discrimination if the wife is in the labor market, while African American couples appear to face
discrimination if the wife stays at home. Similar patterns are true for single-female-headed
households. Single African American women appear to fare better in mortgage lending if they
have children, while white single mothers are at a relative disadvantage.
1
See, for example, Francine D. Blau and Lawrence Kahn 1997, June O’Neill 2003, and Quinn Moore and Heidi
Shierholz 2004.
2
It has been argued that observed differences in risk preferences have a basis in evolutionary biology, in that there
are large differences by sex in returns to investments in reproductive success. For women, the highest returns in
reproductive success have traditionally come from a low-risk strategy of investing in parenting effort, while for men
at times a higher risk strategy of investing in mating effort can have a higher payoff (see Catherine C. Eckel and
Phillip J. Grossman 2002). For examples of gendered approaches to such risk in an economic frame, see Vickie L.
Bajtelsmit and Jack L. VanDerhei 1997; Richard P. Hinz, David D. McCarthy, and John A. Turner 1997; and Nancy
Ammon Jiankokopolos and Alexandra Bernasek 1998. However, Leslie E. Papke (2004) finds no evidence that
women are more conservative investors than men.
5
While there has been a great deal written on the precarious financial situation of women,
there has been relatively little analytical work examining such wealth differentials by gender as
outlined briefly above.3 Particularly important to an analysis of wealth differences by gender is
attention to family structure. Phillip B. Levine, Olivia S. Mitchell, and James S. Moore (2000)
address this in an analysis of the Health and Retirement Study (HRS), a panel dataset that
follows U.S. households during the years before and after retirement. They find sizeable gender
gaps in both current and projected retirement income. They also find that households headed by
unmarried men and unmarried women accumulated considerably less wealth than households of
married couples, even after controlling for differences in household size. Joseph P. Lupton and
James P. Smith (2003) use the HRS to analyze the relationship between household type and asset
accumulation and find that married households have more than twice the net worth of other types
of households.
In this paper, we use OLS and quantile regression to estimate differences in the
distribution of wealth gaps by gender and by family type for the U.S. We examine the extent to
which these gaps can be eliminated by controlling for differences in education, the presence of
children, inheritances, family earnings, and other characteristics. We also analyze a cohort of
younger households and repeated cross sections of households over time to look for evidence
that the relationship between wealth and observable characteristics, family type, and gender may
be changing.
III. DATA
The PSID, collected by the Institute for Social Research at the University of Michigan,
began in 1968 with a national sample of approximately 5,000 U.S. households. Since then, the
PSID has attempted to follow all the individuals from those households, including children of the
3
There has, however, been a great deal of interest in wealth differentials by race (e.g. Francine D. Blau and John W.
Graham 1990; Joseph G. Altonji, Ulrich Doraszelski, and Lewis Segal 2000; Robert Barsky, John Bound, Kerwin
Charles, and Joseph Lupton 2001).
6
original respondents as they begin their own families. Respondents were asked questions on
demographics and employment information every year between 1968 and 1996, and every other
year from 1997 to the present. In addition, the United States National Institute on Aging sponsors
the collection of detailed supplements on household wealth. We focus primarily on wealth data
from 2001, but we examine data from 1994 and 1999 later in the paper to discuss changes in
wealth patterns over the 1990s.
Table 2 provides summary statistics for the dataset.4 The PSID sample for 2001 includes
observations on 5,290 households.5 Individual-level statistics provided are for the household
head, which is generally, but not always, listed as male for married couples.6,7 The average age
of the household head is 50, and household heads average roughly 13 years of education. Single
females are listed as the head for 29 percent of the surveyed households, and single males are
listed as the head for 18 percent of the surveyed households. Families in the sample have an
average total net worth of $193,119. Net worth in the PSID is defined to include the household’s
main home and second home; other rental real estate and land contract holdings; equity in cars,
trucks, boats, and motor homes; farms or businesses; stocks, mutual funds, investment trusts, and
stocks in Individual Retirement Accounts (IRA); savings and checking accounts, money market
funds, certificates of deposit, government savings bonds, and Treasury bills; corporate bonds;
cash value of life insurance policies, valuable collections for investment purposes, and rights in a
trust or estate; less mortgage debt, credit card debt, and other debt on such assets. The one
4
All summary statistics are calculated using the appropriate weights.
To limit the effects of outliers, we trim the distribution and exclude the top and bottom 1 percent.
6
Originally, if a PSID family contained a husband-wife pair, the husband was arbitrarily designated the head to
conform to Census terminology. As new heads are chosen, the head must be at least 16 years old and the person with
the most financial responsibility for the family unit. If this person is female and she has a husband in the family unit,
then he is designated as head. If she has a boyfriend with whom she has been living for at least one year, then he is
head. However, if the husband or boyfriend is incapacitated and unable to fulfill the functions of head, then the
family unit will have a female head. This essentially means that in most cases married couple households are headed
by men.
7
In the PSID, cohabiting opposite-sex couples are treated like married couples except for in the first wave they
appear in the study, where they are labeled as either the boyfriend or girlfriend of the head. Our “married couple”
category therefore includes long-term cohabiting partnerships. However, the PSID does not allow us to identify
same-sex cohabiting couples in our sample.
7
5
important component of wealth that is not included in the PSID measure is pension wealth
(including both public and private pensions). This could be an important omission for our
analysis, since recent evidence suggests large differences in pension wealth by gender (e.g.
Levine, Mitchell, and Moore 2000).
There are several other limitations of the PSID data. First, the PSID does not oversample
the very wealthy, where a great deal of the wealth in the US is concentrated. As a result, the
PSID has been shown to accurately depict household wealth except for the top 1 percent of the
wealth distribution, which is missing from the data (Erik Hurst, Ming Ching Luoh, and Frank P.
Stafford 1998; Wolff 1998; F. Thomas Juster, James P. Smith, and Frank P. Stafford 1999). In
addition, wealth data in any household survey is considered to be particularly noisy (Juster,
Smith, and Stafford 1999), although the PSID uses an innovative unfolding bracket technique to
help deal with non-response and therefore to provide better estimates of wealth.8
Finally, one major disadvantage with any large dataset on household wealth is that it is
not possible to assign ownership of assets to particular individuals within married couples. As
such, our analysis cannot tell us much about the financial well-being of married women, even
though the literature in feminist economics emphasizes the importance of looking at intrahousehold inequality.9
IV. DIFFERENCES IN WEALTH BY GENDER AND FAMILY TYPE
A. Full Sample
First we regress wealth on a constant and indicator variables for family type:
8
If a respondent answers “don't know” or refuses to state a dollar value for a given wealth variable, they are routed
through a series of what are referred to as “unfolding brackets.” For example, if they do not know the balance in
their IRA, they may be asked “Is it more than $50,000 or less than $50,000?” Based on their response to the first
question, they are asked if the balance is more or less than some other value. These answers provide interval values
for the true value of the asset.
9
For example, see Rae Blumberg 1988; Lawrence Haddad and Ravi Kanbur 1990; Amartya Sen 1990; Frances
Woolley 1993; and Shelley Phipps and Peter Burton 1994.
8
Wealthi = α + β1SingleFemalei + β2SingleMalei + εi
(2)
The omitted family structure category is the married couple. Since wealth data in the PSID is
collected at the household level, it is impossible to separate wealth holdings into those assets
owned by the husband and those owned by the wife. Because of this, we assume that marital
assets are consumed jointly and disregard any issues associated with family bargaining and
allocation of resources within the household. Our dependent variable is total net worth.10 These
regressions are run on samples of households with heads aged 25 and older.11
As discussed earlier, the distribution of wealth in the US is highly skewed; many
households have little or no wealth, and a great deal of wealth is concentrated at the top portion
of the distribution. Ordinary Least Squares (OLS) regression provides estimates of differences in
the conditional mean of an outcome variable associated with differences in a covariate. While
this is useful, because the mean can be greatly affected by very high values at the top of the
wealth distribution, estimates from OLS may not provide an accurate characterization about
differences in most of the wealth distribution.
It might also be the case that the covariate of interest has differential effects along the
distribution of the outcome variable. In this paper, we are particularly interested in estimating
differences throughout the distribution of wealth between married households, households
headed by single females, and households headed by single males. It could be the case that most
of the wealth distribution of single households was indistinguishable from that of married
households. At the same time, it could also be true that that the distributions diverge in the upper
25th percent of the distribution, with married couples having higher upper quartile wealth than
single households. Indeed, Figure 1 shows that the difference in wealth by family type is not
uniform across the distribution of wealth. OLS results that indicate mean wealth for single
10
Results using an alternate definition of wealth (total net worth not including equity in main residence) are
qualitatively similar.
11
All regressions are run using the appropriate weights.
9
households was lower would provide a misleading picture of the differences for all but the top
quarter of the wealth distributions.12
Because of this, we estimate four models. We first estimate models by OLS. We then use
quantile regression to estimate models at the 25th percentile, the median, and the 75th percentile
of wealth. Because policymakers may be particularly interested in understanding whether
gender and marital status are important determinants of wealth at the lower tail of the wealth
distribution, quantile regression is particularly useful.13 Whereas OLS estimates the relationship
of a variable to the conditional mean of the dependent variable, quantile regression estimates this
relationship at any point in the conditional distribution, such as the median (the 50th percentile).
By estimating regressions at various quantiles, we estimate a vector of coefficients for different
quantiles of the wealth distribution.
Results for OLS regressions of total net worth can be found in Table 3. Results for
regressions of the 25th, 50th, and 75th percentiles are in Table 3a. We first discuss Column 1 in
each table, which provides results from these simple regressions with no covariates. These
results are exactly equivalent to the simple tabulations of net worth by family type provided in
Table 1, and their analysis reveals that household type plays a large role in wealth differences.
The mean wealth holdings of single-female-headed households are roughly $150,000 less than
that of married couple households. The 25th, 50th, and 75th percentiles of wealth of single-femaleheaded households are $38,000, $106,601, and $220,000 lower than married couple households,
respectively. Similar differences are observed between the wealth holdings of single-maleheaded households and married couple households. The difference between the results at the
12
Gary Chamberlain’s (1994) examination of the union wage premium is a good illustration of how OLS can be
misleading. He estimates a union wage premium of 28 percent at the first decile of income. The premium declines
with income and is negligible at the top decliles. The OLS estimate of the mean union wage premium is 15.8
percent. While it is correct to interpret that the mean wage of union members is 15.8 percent higher than that of nonunion workers, it would be incorrect to conclude that unions typically increase the wages of workers, because the
OLS estimate is being driven by differences seen only at the lower tail of the conditional distribution.
13
See Roger Koenker and Kevin F. Hallock (2001) for a non-technical discussion of quantile regression, and see
Roger Koenker and Gilbert Bassett (1978) for the econometric derivation.
10
mean, the 25th percentile, 50th percentile, and the 75th percentile again reflect the skewed nature
of wealth distributions.
In these simplest regressions, gender appears to be a less important factor than marital
status in determining differences in wealth. The F-statistic at the bottom of each column is from
a Wald test of the linear hypothesis that the coefficient on single male is equal to the coefficient
on single female.14 For all four models without covariates, the value of this statistic is below the
appropriate critical value, indicating that there is no statistical evidence that the wealth holdings
of single-female-headed households are different from those of households headed by single
men.
However, there are a variety of factors that are likely to be correlated with both family
type and net wealth. In order to compare the wealth of similar households that differ only by the
gender and marital status of the household head, we next estimate equations of the form
Wealthi= α + β1SingleFemalei + β2SingleMalei + δXi + εi (3)
where the X vector includes a number of household characteristics. We control for life cycle
effects on wealth accumulation by controlling for the age and age squared of the household head,
as well as indicator variables for whether the household head has children under the age of 18,
between the ages of 18 and 24, or older than 25. We also include a dummy variable that indicates
whether anyone in the household has received a gift or inheritance of $10,000 or greater in the
past five years. In addition, Equation (3) controls for years of educational attainment of the
household head, as well as race and Hispanic ethnicity. It also includes a measure of whether the
household head was married before; this indicator variable is equal to one for widowed,
divorced, and separated households and is also equal to one for a married head who is not in his
or her first marriage.
14
See Jeffrey M. Wooldridge (2002).
11
We are also interested in the extent to which differences in wealth are due to differences
in earnings. However, because households choose their labor supply as a function of wealth,
earnings are endogenous. Therefore, a simple regression coefficient on earnings cannot be
interpreted as causal. The same concern may apply to marital status and the presence of children.
Because of this, these regressions should be thought of as descriptive. They are meant to identify
correlations and are not meant to imply causality. Instead, they are provided to examine how
much of the difference in wealth across gender and family type is associated with differences in
other observable characteristics. Because family earnings are particularly problematic, we
estimate these regressions both with and without family earnings. The results without earnings
are in Column 2 of Tables 3 (for OLS) and 3a (for quantile regressions), and the results with
earnings are in Column 3.
The estimated coefficients on the covariates are of the signs that would be expected
across the four models. Wealth increases with age but at a decreasing rate. Also, in general, the
relationship between children and wealth holdings depends critically upon the age of those
children, providing further evidence in support of life-cycle theories of wealth accumulation. The
presence of minor children is not statistically correlated with wealth levels,15 but having children
aged 18-24 is associated with lower wealth for all percentiles except the 25th, consistent with a
story that suggests that individuals save in order to pay for children’s college educations. The
presence of grown children is associated with greater wealth holdings. Higher levels of
education are associated with higher wealth, while previous marriages are negatively correlated
with wealth holdings. Consistent with previous research, African American households have
15
The predicted effect of children on wealth is ambiguous. It has been argued that people with children should save
more, since children encourage forward-looking behavior. Bequest motives might also cause individuals with
children to save more. However, consumption may increase due to goods that children need, and parents may reduce
labor supply when children are small. In addition, these effects might be expected to vary over the life cycle. See
James P. Smith and Michael P. Ward (1980) or Joseph P. Lupton and James P. Smith (2003) for detailed arguments.
12
lower levels of wealth (e.g. Blau and Graham 1990; Barsky et al. 2001). Not surprisingly, receipt
of an inheritance is associated with significantly higher wealth.
Though we observed similar patterns for most of the covariates in the OLS and quantile
regressions, in certain instances the magnitudes varied a great deal. For example, the large
coefficient on years of schooling on wealth holdings at the mean ($20,965 in Column 3 of Table
3) is significantly larger than the coefficient at the median ($8,819 in Column 3 of the second
panel in Table 3a), suggesting that these effects in the OLS regression are driven heavily by
individuals at the upper end of the wealth distribution.
The estimated difference in wealth across family types decreases from Column 1 to
Column 2 when all covariates except earnings are added. In the OLS results, the coefficient on
single female falls from -$150,382 to -$125,294. Sizeable decreases in this coefficient also occur
at the median and 75th percentiles, though not at the 25th percentile. However, the gap in wealth
due to family type does not disappear. In all four specifications, households headed by single
women have significantly lower wealth than households headed by married couples. Not
surprisingly, the gap increases in magnitude as we move to higher percentiles of the wealth
distribution. These results suggest that the wealth gap between single-female-headed households
and married couples is sizable but that a significant portion of the gap may be reflecting
differences in observable characteristics that are correlated with gender and marital status, like
education and inheritances. However, even when holding these observable characteristics
constant, a large wealth gap exists between single households and married households.
In addition, as is evidenced by the F-statistics at the bottom of each column, once these
observable characteristics are controlled for, single-female-headed households have significantly
lower levels of wealth than single-male-headed households. This is true at the mean as well as at
the 25th, 50th, and 75th percentiles. The set of results with and without covariates suggests that
there is no gender gap in the raw distribution of wealth of single male and single female
13
households. However, if we were to compare households from each of these two groups with
identical characteristics, we would observe a strong and significant gender gap.
Inclusion of family earnings (Column 3) plays a large role in reducing the estimated
wealth gap between single and married households across the distribution. In all models, the
estimated coefficients decline in magnitude, though they are still sizable and statistically
significant. The coefficient of -$31,478 on single-female households in the median regression
implies that even after controlling for observable differences, including earnings, median wealth
of households headed by single females differs from median wealth of married households by
roughly 40 percent of overall median wealth. The inclusion of earnings has little effect on the
gender wealth gap among single households across most of the distribution, evidenced by the
fact that the difference between the coefficients on single-female households and single-male
households remains approximately the same.
The large effect of family earnings on the marital wealth gap but not the gender gap could
be due to the difference across these types of households in the number of adults, since married
couple households will have more adults than single households. To explore this further, we ran
an additional set of regressions with an alternate dependent variable of wealth divided by the
number of adults in the household.16 It is not clear that this is the “correct” measure of wealth if
we are concerned about gender differences in well-being. For instance, two individuals may not
need twice as much wealth to maintain the same level of well-being because of economies of
scale.17 This is even more important given that our measure of total net worth includes equity in
the family’s main home. However, we present results from this regression in Table 4 (for OLS)
and 4a (for quantile regressions) to provide a comparison to our earlier results.
16
We also re-estimate regressions with our original dependent variable, but with an additional control for the
number of adults in the household. The results are very similar to the results presented in Tables 3 and 3a. These
results are available from the authors upon request.
17
We have also estimated our models adjusting wealth by alternative equivalence scales (see Constance Citro and
Robert T. Michael 1995). Our main results are similar to those in Table 3, though they differ slightly in some
specifications. These results are available upon request.
14
The patterns that emerge for the control variables are similar to those found in Table 3.
However, the effects of family type differ a great deal. If the dependent variable of interest is
wealth per the number of adults in the household, it appears that single-male-headed households
generally accumulate the most wealth, once we control for all variables. In all cases except the
25th percentile, the wealth of single-male-headed households is significantly higher than that of
married couples. In addition, once we control for earnings, there is no statistically significant
difference between the wealth of single-female-headed households and married couples in this
alternative specification at either the mean or the median, and there are opposing effects at the
two ends of the distribution. Single-female-headed households fare worse than married couple
households at the 25th percentile but fare better than married couple households at the 75th
percentile, once all observable characteristics are controlled for. However, it is clear that gender
still plays a large role, as in all cases (except at the 75th percentile) the wealth holdings per capita
of single-female-headed households are statistically lower than those of single-male-headed
households.
One potential implication of these results is that, for the poorest women, single
motherhood is most associated with lower levels of wealth. When controlling for earnings and
other observable characteristics, the 25th percentile of per person wealth in households headed by
single women is about $5,000 less than the 25th percentile of per person wealth among married
households. Given that a measure of per person wealth “over-controls” for family size, this
estimate likely understates the difference in well-being by family type at the lower end of the
wealth distribution. Those women who are the most vulnerable financially are at even greater
financial risk when they are unmarried.18
18
The R-squareds on the regressions in Tables 3 and 4 are on the lower side, ranging from 0.09 to 0.25, suggesting
that most of the differences across households in wealth accumulation are not explained by the variables in our
model. One possibility is that a great deal of wealth comes from inheritances. In fact, J. Bradford DeLong (2003)
estimates that 43 percent of wealth in the US is inherited. Though we control for whether the household received an
inheritance in the past five years, it is likely that this measure does not fully reflect the extent to which inheritances
affect wealth.
15
B. Does the wealth gap persist for the youngest cohort?
While the results presented above for the full PSID sample control for age, there are
reasons to believe that gender and family type differences in wealth holdings among younger
cohorts might differ systematically from those found in the full sample. First, rates of completion
of higher education have converged among men and women. Between 1990-1 and 2000-1, the
number of bachelor's degrees awarded to men increased by 6 percent, while those awarded to
women rose by 21 percent (NCES 2002). Currently more women than men earn associate,
bachelor's, and master's degrees. Second, the past 40 years have seen large increases in labor
force participation rates of women between the ages of 25 and 40. This is partly due to the
increase in labor force participation of women with small children. In 1960, 19 percent of
women with children under the age of 6 were in the labor force. By 1999, this rate had increased
to 61 percent (Francine D. Blau, Marianne Ferber, and Anne Winkler 2002). Finally, there have
been significant increases in women’s age at first birth, which rose 3 years between 1970 and
2000 (NCHS 2002).
Tables 5 (for OLS) and 5a (for quantile regressions) provide results from net worth
regressions that restrict the sample to those households with a head who was between the ages of
25 and 39 in 2001. The results with no covariates still show that single households have
significantly lower levels of total net worth than married households and that there is no wealth
gap by gender except at the 75th percentile. Similar to the results for the full sample, when all
covariates other than earnings are added, the negative effect of being in a single-headed
household relative to married couples declines but is still present. However, when we control for
differences in earnings among the younger sample, there are no statistical differences in wealth
by family structure or gender. The coefficients on single-female and single-male-headed
16
households are small and not significant, and the F-test of differences between single females
and single males reveals no evidence of any statistical differences by gender.
C. Disentangling age and cohort effects
Our results in the previous sections suggest that even after controlling for differences in
observable characteristics, including earnings, there are large wealth gaps by gender and marital
status throughout the wealth distribution. In addition, our results show that these gaps do not
appear when analyzing the sub-sample of the population that is aged 25-39. Although simple
tabulations of the wealth holdings of this younger cohort show a large gender wealth gap, single
women in this age group do not have lower average wealth than either married couples or maleheaded households once we control for earnings and other observable characteristics.
There are two possible explanations for this. First, this could be a cohort effect. The
current population of young women could be saving more or receiving higher rates of return on
their savings than their counterparts in earlier cohorts. This would imply that future generations
of retired women might be in an improved financial position than the current cohort of retired
women. A second possibility is that wealth gaps do not emerge until later in life. Previous
research on wealth has discussed the difficulty of disentangling age, time, and cohort effects (e.g.
John Ameriks and Stephen Zeldes 2001). In particular, since the age of individual i in period t is
equal to the current year minus her year of birth, it is impossible to identify all three effects
simultaneously without making additional identifying assumptions.
To partially address this, we use a sample of pooled data from the 1994, 1999, and 2001
waves of the PSID. We control for whether the individuals are in the “young” age group (ages
25-39) as well as for year affects that will pick up any changes in wealth accumulation patterns
over this time period. We also interact the year effects and the age group effects with our
variables for gender and family type. This will allow us to identify whether the relationship
17
between wealth, gender, and marital status has changed over this time period. The 1990s are a
particularly important period of time to examine, since an unprecedented appreciation in the
value of stocks over this time period led many households to accrue capital gains that were both
large and unexpected. In addition, this period witnessed a large increase in the number of
households investing in stocks (e.g. James M. Poterba 2001; Purvi Sevak 2004).
Results from this specification, controlling for earnings and all other covariates, are found
in Table 6.19 The coefficients on the year dummies suggest that average wealth levels had been
rising over the 1990s for the upper half of the wealth distribution. The coefficient on the
indicator for whether the individuals are between 25 and 39 is negative and generally statistically
significant, again providing evidence of life-cycle motivations in wealth accumulation. The
interaction between age and the year effects suggests that although wealth accumulation has been
increasing over the 1990s, this effect is significantly lower for young households.
The coefficient on the interaction between the “single female” and “young” categories is
positive and statistically significant, largely offsetting the negative coefficient on “single
female.” The same pattern emerges for young single males. This is consistent with our results for
2001 presented in Tables 5 and 5a and suggests that wealth gaps due to both gender and marital
status do not persist in the younger cohort. This could be an age effect or a selection effect. One
possibility is that differences in wealth grow over the life cycle. An alternate possibility is that
this is due to a recent trend in the US of delaying first marriages. It is possible that those young
people who married are systematically different from singles on some omitted characteristics that
are correlated with wealth.
Finally, we analyzed the interaction of our family type variables with the year effects. At
the mean, 25th percentile, and median, these variables are generally not significant, suggesting
19
The coefficients on the control variables are largely unchanged in these specifications and are omitted from the
table.
18
that the relationship between wealth, family type, and gender did not change over the 1990s, a
decade that saw large shifts in wealth accumulation patterns. However, the negative and
significant coefficients in the regression of the 75th percentile imply that differences in the 75th
percentile of wealth between married households and both groups of single households grew
over the 1990s. This is not surprising, since the largest wealth gains over the period were made
in the stock market. In order for a household to have made such gains, they had to have had
significant investments at the start of the period. This is mostly likely to have been the case for
households in the upper part of the wealth distribution, and our results would be consistent with
the observation that married households were more likely to have these investments than single
individuals.
V. CONCLUSION
Despite the legal, social, and economic gains that women in the United States have made
in recent decades, a statistically and qualitatively significant wealth gap persists between
households based on both gender and marital status. Our analysis of data from the 2001 PSID
reveals households headed by married couples have more than twice the mean wealth as
households headed by single females and that these large differences are present across the
wealth distributions. Using OLS and quantile regressions, we find that these wealth gaps are
reduced but not eliminated by the addition of controls for life-cycle factors, education, and
family earnings. In fact, differences in observable characteristics between family types are
associated with no more than half of the family type wealth gap. In addition, our quantile
regression results imply very different magnitudes of the wealth gap than do our OLS
regressions, illustrating the importance of using quantile regression when examining a variable
with a skewed distribution, like wealth. The wealth holdings of single females and single males
19
appear quite similar throughout the wealth distribution, but inclusion of control variables leads to
large and significant gaps in wealth among singles by gender.
In contrast with these results for the entire sample, an analysis of young households
whose heads are between 25 and 39 years of age indicates that observed wealth gaps disappear
for this cohort when controlling for observable characteristics correlated with gender, family
type, and wealth. This suggests that these younger households may be systematically different
than their older counterparts. Perhaps they are saving more or investing more aggressively.
Increases in female educational attainment, increases in the age at marriage, and delays in
childbearing, are other possible explanations for why we do not observe wealth gaps for this
younger cohort. In this case, other countries which are currently making gains in female
educational attainment and experiencing similar demographic shifts may find convergence in
wealth patterns by gender and marital status over time as well.
However, it could instead indicate that these wealth gaps do not emerge until later in life.
Our data do not allow us to adequately distinguish between these two possibilities, but
determining which is the case has important policy implications for the financial well-being of
women. Cross-country comparisons might help shed further light on this important question.
20
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23
Table 1:
Average Household Wealth as Measured in the 2001 Panel Study of Income Dynamics
(n=5,290 households)
Mean
25th Percentile
Median
75th Percentile
All households
$193,119
$10,650
$76,000
$236,500
By type of household
Single female head
Single male head
Married male head
$112,547
$119,861
$262,929
$1,500
$1,900
$39,500
$29,500
$29,000
$136,101
$135,000
$122,250
$355,000
Note: Households with wealth in the top and bottom 1% of the total wealth distribution and any missing data are dropped.
24
Table 2:
Summary Statistics in 2001 Panel Study of Income Dynamics
Mean
127,601
193,119
0.29
0.18
0.53
0.42
49.93
0.33
0.19
0.39
13.05
0.13
0.06
47,366
0.05
Non-Housing Net Worth
Total Net Worth
Single Female Head
Single Male Head
Married Head
Married Before
Age of Head
Has Children < Age 18
Has Children Ages 18-24
Has Children Ages 25+
Years of Education
Black
Hispanic
Family Earnings
Received Inheritance
N
Standard Deviation
249,059
303,587
----15.88
---2.94
--55,948
--
5,290
Notes: Households with wealth in the top and bottom 1% of the total wealth distribution and any missing data are dropped.
For married households, age, education, race, and ethnicity are of the household head.
25
Table 3:
OLS Regression of Networth of Households Ages 25+ in 2001 PSID
Single Female
(1)
-150,382
**
(10729)
Single Male
-143,069
(2)
-125,294
**
(10595)
**
(12503)
-75,288
14,418
**
-76
**
-38,047
Has Children < Age 18
Has Children Ages 18-24
Has Children Ages 25+
Years of Ed
**
**
Had Inheritance
-39,790
4,361
(9275)
(8775)
**
-29,471
(10738)
(10407)
19,909
32,049
(12883)
(12580)
**
(1649)
Hispanic
-55
11,182
-58,231
20,965
**
**
**
**
**
(1705)
**
-49,954
(11580)
(11540)
-17,183
-2,864
(15622)
(15304)
137,895
**
(16)
(10289)
25,925
Black
12,371
(10500)
-21,186
**
(1696)
(16)
Previously Married
-39,649
(12289)
(1728)
Age squared
**
(11321)
(12041)
Age
(3)
-87,307
**
(27326)
135,012
**
**
(27002)
Earnings
1.07
**
(0.12)
Constant
262,930
**
-595,698
**
-557,172
(7740)
(50209)
(48652)
R-sq
0.06
0.23
0.26
F Test
0.35
16.55
N
5,290
5,290
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data.
For married households, age, education, race, and ethnicity are of the husband.
26
**
15.25
5,290
**
**
Table 3a:
Quantile Regression of Networth of Households Ages 25+ in 2001 PSID
Single Female
(1)
-38,000
25th Percentile
(2)
**
-38,516 **
(1041)
Single Male
-37,600
(3)
-19,776
(3239)
**
(1237)
-31,470
-12,385
**
(3626)
Age
4,485
-27
3,861
**
-12,884
-20
**
Has Children Ages 18-24
Has Children Ages 25+
-15,264
**
7,791
**
-38
**
-21,880
**
-35
**
**
-22,287
**
-35,732
7,991
**
-10
(15)
**
-41,511
4,775
3,197
19,406
(5133)
(5027)
(10322)
(9590)
802
608
-8,872
-8,744
-5,803
-14,432
(2911)
(2842)
(5445)
(5328)
(10929)
(10214)
7,167
15,609
13,271
31,420
(6214)
(6104)
(12040)
(11175)
7,099
*
**
(3286)
3,376
**
13,627
**
(411)
-14,735
**
(762)
-33,152
**
(3230)
(3122)
(5845)
-7,092
-4,496
(4855)
(4763)
53,420
**
-168,476
(14443)
0.03
0.08
5,290
0.08
3.50
5,290
**
-154,280
**
0.10
4.16
5,290
For married households, age, education, race, and ethnicity are of the husband.
**
(1646)
-56,516
**
**
**
**
557
-15,190
3,039
(9239)
(17903)
(17464)
-318,496
(3463)
(26041)
0.05
0.01
5,290
0.15
8.09
5,290
27
-45,956
(9345)
**
99,211
231,004
**
(9937)
1.02
136,101
17,033
**
-263,443
0.17
6.21
5,290
**
**
(10959)
**
(20668)
184,685
**
(19428)
1.48
**
**
(0.08)
**
(25591)
**
**
(1589)
(0.04)
**
15,277
-6,808
**
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data.
-29,224
*
(11804)
(10147)
(13865)
*
22,354
**
(773)
(0.02)
(538)
8,819
**
(9632)
(5722)
110,848
**
(5198)
0.65
**
**
**
(1687)
(16)
**
**
(11722)
(1816)
(8)
-38,881
-496
Earnings
Pseudo R-sq
F Test
N
10,314
**
**
(12274)
(2710)
55,541
**
-18
-85,913
-761
-18,385
39,500
**
(16916)
**
(10694)
(2755)
(5570)
Constant
5,563
-232,750
(922)
(8)
**
**
(6370)
(939)
**
-14,645
(11039)
(10269)
(400)
Had Inheritance
**
(6383)
(14243)
(3)
-66,471
(4936)
6,216
Hispanic
-42,813
75th Percentile
(2)
**
-126,292 **
(5047)
(3412)
Black
**
(7702)
**
(5894)
(1)
-220,000
(2573)
6,055
Years of Ed
-107,601
(5)
(2653)
Has Children < Age 18
**
(5867)
(522)
(5)
Previously Married
(6497)
(3519)
(540)
Age squared
**
(3222)
50th Percentile (Median)
(1)
(2)
(3)
-106,601 **
-62,440 **
-31,478
**
355,000 **
-413,044
(7761)
(52830)
0.06
0.44
5,290
0.19
8.92
5,290
**
-393,276 **
(49443)
**
0.22
4.72
5,290
**
Table 4:
OLS Regression of Per Adult Networth of Households Ages 25+ in 2001 PSID
Single Female
(1)
-22,934
(2)
-16,007
**
(7840)
Single Male
-19,424
**
**
(7323)
(7568)
15,230
32,166
(9732)
(9805)
Age
8,224
**
(1209)
Age squared
-39
Previously Married
Has Children < Age 18
Has Children Ages 18-24
Has Children Ages 25+
**
-9,249
(6374)
(6279)
5,671
2,430
(5705)
(5521)
**
-42,623
(5992)
(5940)
4,973
10,742
(8537)
(8443)
**
(1041)
13,278
**
-37,628
(9113)
326
7,130
(8752)
(8686)
78,855
**
**
**
**
(1070)
(9121)
Hispanic
Had Inheritance
-29
-8,420
-41,562
**
(1195)
(11)
15,635
Black
7,251
(11)
-38,685
Years of Ed
(3)
2,045
**
(15884)
77,485
**
**
(15766)
Earnings
0.51
**
(0.06)
Constant
R-sq
F Test
N
123,163
-383,443
**
**
-365,006
(3758)
(34427)
(33906)
0.0034
0.09
0.1875
8.3
0.2048
7.8
5,290
5,290
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data.
For married households, age, education, race, and ethnicity are of the husband.
28
**
5,290
**
**
Table 4a:
Quantile Regression of Per Adult Networth of Households Ages 25+ in 2001 PSID
Single Female
(1)
-15,950
**
(733)
Single Male
-15,750
25th Percentile
(2)
(3)
-14,169 ** -5,272
(1716)
**
(869)
-10,482
**
(1899)
Age
2,186
**
(287)
Age squared
-12
-4,892
Has Children < Age 18
Has Children Ages 18-24
**
**
1,657
**
-7
-5,951
-4,743
9,342
(3494)
(2911)
3,631
**
-12
**
-8,876
**
1,209
5,577
(1560)
(2799)
-13,852
**
(1618)
*
5,148
**
(1912)
**
(214)
-9,995
-4,758
2,007
**
-8,081
**
**
**
(7688)
5
-22
(4)
(9)
-8,168
4,495
-14,516
4,857
(3390)
(2789)
-13,326
**
5,177
-17,820
(3191)
(2611)
13,438
*
-27,916
**
*
(3001)
0.30
(5532)
(4516)
0.52
**
-89,694
(380)
(7591)
0.0155
0.04
5,290
0.0639
3.41
5,290
**
-77,431
*
0.0781
3.28
5,290
**
**
**
*
61,725
**
-176,517
(1939)
(14218)
0.0165
0.04
5,290
0.1277
8.36
5,290
**
-140,959
**
0.1459
5.95
5,290
**
**
-43,029
**
133,084
**
(11967)
0.79
**
**
(0.04)
**
**
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data. For married households, age, education, race, and ethnicity are of the husband.
29
9,867
**
(11732)
**
(12242)
(11692)
**
-27,927
(986)
(0.02)
(8083)
**
(5779)
-13,228
132,796
**
12,535
(6851)
-592
68,007
**
(6957)
(10469)
**
(5945)
(7067)
(4158)
**
-38,832
29,274
4,560
67,340
**
(6998)
-43,377
**
(9)
1,655
-469
**
24
(6462)
(5149)
33,783
**
(952)
**
*
1,500
(6373)
13,269
**
**
(1030)
(5859)
-907
**
**
(5819)
(354)
**
14,719
(7381)
(1055)
5,342
**
4,348
6,838
**
(2293)
(0.01)
R-sq
F Test
N
**
(14556)
(2790)
Earnings
**
-65,500
(6631)
(2636)
32,509
17,250
**
(6462)
-2,570
(2900)
Constant
**
(12201)
75th Percentile
(2)
(3)
-14,398 ** 19,072
(422)
(2438)
-22,409
**
(1)
-44,500
(2258)
(417)
(1772)
1,990
(2971)
7,572
**
(241)
(1682)
**
(5)
104
**
**
(513)
(1491)
3,365
Had Inheritance
(4298)
(2694)
(2753)
(1800)
Hispanic
-38,725
(2011)
(3203)
(1500)
3,029
Black
-1,468
(3)
(1552)
Years of Ed
(3640)
(1420)
-5,207
Has Children Ages 25+
(1836)
(304)
(3)
Previously Married
**
50th Percentile (Median)
(1)
(2)
(3)
-39,725 ** -15,637 **
1,823
163,500
**
-306,261
(6661)
(30601)
0.008
1.62
5,290
0.1728
5.43
5,290
**
-176,452
(29813)
**
0.1919
0.29
5,290
**
Table 5:
OLS Regression of Networth of Households Ages 25-39 in 2001 PSID
(1)
Single Female
-82,483
(2)
-61,678
**
(12017)
Single Male
-60,138
(3)
**
(12123)
-33,644
**
(14497)
-13,531
(13268)
**
(12156)
5,359
(13012)
Age
10,257
(1409)
**
8,485
(1298)
**
Previously Married
-22,826
(11588)
7,325
(12160)
-43,629
(12238)
10,807
(2092)
-28,118
(17776)
-39,173
(10142)
132,617
(42749)
**
-19,918
(11152)
5,908
(11697)
-39,475
(11645)
3,932
(1885)
-20,582
(17719)
-26,820
(10432)
128,639
(40257)
1.25
(0.19)
-300,934
*
Has Children < Age 18
Has Children Ages 18-24
Years of Ed
Black
Hispanic
Had Inheritance
**
**
**
**
Earnings
Constant
R-sq
F Test
N
123,517
-363,060
**
**
(8681)
(52239)
(45796)
0.0366
2.45
1750
0.1459
3.59
1750
0.22
1.66
1750
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data.
For married households, age, education, race, and ethnicity are of the husband.
30
*
**
**
**
**
**
**
Table 5a:
Quantile Regression of Networth of Households Ages 25-39 in 2001 PSID
Single Female
Coeff
-11,000
Single Male
-10,425
25th Percentile
Coeff
**
-10,908 **
(534)
(2340)
**
(544)
-7,721
**
(2205)
Age
1,000
Previously Married
Has Children < Age 18
Has Children Ages 18-24
Years of Ed
Hispanic
Had Inheritance
(1855)
(2763)
1,869
-31,400
(1736)
(2770)
1,034
(4412)
**
-19,059
**
(4336)
2,829
**
**
(10162)
2,130
-37,637
(7725)
**
(8329)
7,780
**
4,987
(7584)
**
6,021
(808)
-1,901
-1,666
-3,955
-4,330
-17,164
(2057)
(1569)
(4166)
(3212)
(8537)
(7632)
2,522
5,245
6,026
6,328
11,537
14,408
(1750)
(1368)
(3665)
(2808)
(7268)
(6479)
95
-52
-1,086
-4,920
-24,064
-13,673
(3518)
(2996)
(7266)
(5874)
(14733)
-164
3,924
-61
7,415
(318)
(262)
(652)
(519)
(1486)
-3,334
-2,137
-11,806
(2157)
(1674)
(4355)
-1,703
2,830
(2873)
(2160)
**
**
33,172
**
(3035)
0.49
**
-35,031
(7350)
**
-47,872
(5561)
-6,301
**
-30,641
*
(3331)
(8559)
-6,271
-976
-30,849
(5810)
(4546)
(11867)
88,019
**
**
**
(7462)
74,013
161,703
**
(5732)
0.90
**
(0.01)
**
(3410)
(8449)
**
(305)
Earnings
(271)
(10235)
-86,200
Coeff
-3,351
(394)
23,721
11,000
(3532)
3,400
75th Percentile
Coeff
**
-56,034 **
(153)
(3893)
Constant
Coeff
-115,000
(207)
939
Black
**
50th Percentile (Median)
Coeff
Coeff
Coeff
-35,900 ** -28,104 **
763
Coeff
442
**
41,000
(1400)
**
-103,275
(15348)
**
-80,265
(11844)
**
**
3,098
**
(1248)
**
-23,145
**
(7741)
**
-11,062
**
191,556
(10306)
(15318)
**
(13265)
1.43
**
**
(0.06)
**
138,000
**
(5322)
R-sq
0.0103
0.0202
0.0522
0.0332
0.073
0.1262
0.0526
F Test
0.76
0.0202
0.52
1.78
3.39
**
0.49
5.48
**
N
1750
1750
1750
1750
1750
1750
1750
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
Sample excludes households with wealth in the top or bottom 1% and any missing data. For married households, age, education, race, and ethnicity are of the husband.
31
-18,643
(13381)
**
(0.03)
**
**
(722)
-236,148
**
(34171)
5.48
3.72
1750
-216,249
(29187)
*
0.1985
0.96
1750
**
Table 6:
OLS and Quantile Regression of Networth of Households Ages 25+ in 1994, 1999, and 2001 PSID
Single Female
OLS
-106,185 **
(9072)
Single Male
Young
Year=1999
-77,464
(2511)
**
Single Female*1999
Single Male*1999
Single Female*2001
Single Male*2001
Young*1999
R-squared
N
F male
F female
**
(9666)
(2779)
(3968)
(8519)
-1,314
-1,904
46,811
(1855)
(2760)
(5804)
20,736
**
32,302
(4585)
-63,310
-11,498
-26,193
**
**
-120,285
2,830
8,947
(1832)
(2764)
(5829)
**
**
71,108
-9,366
1,833
-1,115
-48,980
(11381)
(3005)
(4356)
(9014)
-11,670
3,489
4,022
-44,665
(11924)
(3639)
(5276)
(10754)
-14,653
-4,752
-9,452
(12232)
(3078)
(4442)
(8832)
**
-40,922
-1,643
-952
514
-61,083
(13834)
(3744)
(5393)
(11138)
-4,330
-3,914
-7,961
(2666)
(3834)
(8186)
-19,496
**
-22,736
**
120,528
101,095
-7,331
-11,366
**
(2743)
**
38,935
**
35,726
**
(3950)
71,655
**
(2742)
63,489
-23,992
**
105,240
**
119,697
(3101)
(4452)
(9164)
0.28
17,187
3.35
0.00
0.07
17,187
2.72
0.23
0.17
17,187
5.67
2.20
0.23
17,187
0.23
0.00
*
Notes: ** Denotes statistical significance at the 5% level and * at the 10% level.
"Young" is ages 25-39. Sample excludes households with wealth in the top or bottom 1% and any missing data.
All regressions include all control variables found in earlier regressions.
32
**
**
**
**
**
**
**
**
(8435)
(10043)
*
**
(8283)
(3958)
**
**
(9215)
(10386)
(8310)
Young*Single Male
-52,651
**
(7231)
-10,266
(9762)
Young*Single Female
(3630)
75th Percentile
-63,802 **
(3158)
(8718)
Young*2001
-29,229
50th Percentile
-55,650 **
(10754)
(10008)
Year=2001
25th Percentile
-32,686 **
**
**
Figure 1: Wealth Distribution by Family Type, 5th to 95th Percentiles
PSID 2001.
1,400,000
1,200,000
1,000,000
Wealth
800,000
600,000
400,000
200,000
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
-200,000
Percentile
Single Female
33
Single Male
Married
15
16
17
18
19